A unified knowledge compiler to provide support the scientific community

摘要

The scientific community represents an insatiable network with important needs related to the searching of information. The ever-broadening amount of domain-scientific on-line information that can be found requires increasingly sophisticated frameworks to manage it. Nevertheless, these frameworks are usually focused on specific useful functionalities and work more like expert systems than general purpose approaches. In order to ease the research process to the scientific community, the Unified Knowledge Compiler (UNIKO) framework is presented. This framework includes, among other functionalities, the recommendation of articles and authors related to a specific field of application, the evaluation of reputation scores of articles and authors, and the sentiment analysis of the texts of the articles. UNIKO is built as a hybrid framework based on Knowledge-Based Systems and Content-Based Recommendation Systems. In order to evaluate the performance of the system, several experiments have been done. The first experiment is developed to illustrate the reputation scoring task. The second one addresses the sentiment scores calculation based on a lexicon which is supported by a Convolutional Neural Network. The last experiment shows the recommendation tasks based on specific similarity measures and unsupervised learning.